up

src/core/awareness_engine.py

@@ -2,7 +2,7 @@ from typing import Dict, Any
 import torch
 import torch.nn as nn
 import numpy as np
-from .states import AwarenessState
+from .states import AwarenessState, AwarenessLevel
 
 class AwarenessEngine:
     def __init__(self):
@@ -15,14 +15,27 @@ class AwarenessEngine:
     async def process(self, input_state: Dict[str, Any]) -> AwarenessState:
         attention_vector = self._compute_attention(input_state)
         awareness_level = self._calculate_awareness(attention_vector)
+        level = self._determine_awareness_level(awareness_level)
         
         return AwarenessState(
             attention_vector=attention_vector.detach().numpy(),
             awareness_level=awareness_level,
             cognitive_state=self._process_cognitive_state(input_state),
             emotional_valence=self._compute_emotional_valence(input_state),
-            consciousness_level=0.8
+            consciousness_level=0.8,
+            level=level
         )
+    
+    def _determine_awareness_level(self, awareness_level: float) -> AwarenessLevel:
+        if awareness_level > 0.8:
+            return AwarenessLevel.TRANSCENDENT
+        elif awareness_level > 0.6:
+            return AwarenessLevel.INTEGRATED
+        elif awareness_level > 0.4:
+            return AwarenessLevel.REFLECTIVE
+        elif awareness_level > 0.2:
+            return AwarenessLevel.PERCEPTUAL
+        return AwarenessLevel.BASIC
         
     def _compute_attention(self, input_state: Dict[str, Any]) -> torch.Tensor:
         return torch.ones(256)

src/core/integration_manager.py

@@ -61,10 +61,10 @@ class IntegratedState(Generic[T]):
     teleological_vector: Optional[Dict[str, float]] = None
 
 
-from typing import Dict, Any
+from typing import Dict, Any, List
 import torch
 import torch.nn as nn
-from .states import AwarenessState
+from .states import AwarenessState, AwarenessLevel
 
 class IntegrationManager:
     def __init__(self):
@@ -78,11 +78,24 @@ class IntegrationManager:
         if not isinstance(awareness, AwarenessState):
             raise ValueError("Primary awareness state must be of type AwarenessState")
             
+        emergent_properties = await self._generate_emergent_properties(awareness)
+        
         return {
             "integrated_state": self._integrate_awareness(awareness),
             "consciousness_level": awareness.consciousness_level,
-            "emotional_context": {"valence": awareness.emotional_valence}
+            "emotional_context": {"valence": awareness.emotional_valence},
+            "emergent_properties": emergent_properties
         }
+    
+    async def _generate_emergent_properties(self, primary: AwarenessState) -> Dict[str, Any]:
+        return {
+            "awareness_depth": self._calculate_awareness_depth(primary),
+            "integration_level": primary.awareness_level,
+            "consciousness_state": str(primary.level)
+        }
+    
+    def _calculate_awareness_depth(self, primary: AwarenessState) -> float:
+        return primary.level.value / len(AwarenessLevel)
         
     def _integrate_awareness(self, awareness: AwarenessState) -> Dict[str, Any]:
         return {

src/core/states.py

@@ -1,6 +1,14 @@
 from dataclasses import dataclass
 from typing import Dict, Any
 import numpy as np
+from enum import Enum
+
+class AwarenessLevel(Enum):
+    BASIC = 1
+    PERCEPTUAL = 2
+    REFLECTIVE = 3
+    INTEGRATED = 4
+    TRANSCENDENT = 5
 
 @dataclass
 class AwarenessState:
@@ -8,4 +16,5 @@ class AwarenessState:
     awareness_level: float
     cognitive_state: Dict[str, Any]
     emotional_valence: float
-    consciousness_level: float
+    consciousness_level: float
+    level: AwarenessLevel = AwarenessLevel.BASIC